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How to change x-axis ticks labels in stackedplot?

  h = stackedplot(rand(6,3));   I want to set x-axis ticks according to my own defined set i.e., instead of 1:6, I want to replace x-axisticks [1, 2,3 ,4,5,6] to ['A', 'S','T', 'AAA', 'BBB', 'ZZZ'] , by rotating it to 90 degree that is vertically insted of horizontally?     NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research. There does not seem to be an easy way to set the  XTick  or  XTickLabel  of a  StackedLineChart  object (such as what's created by  stackedplot ):   data = rand(6,3); h = stackedplot(1:6,data); % try a couple of things, neither of which work try set(h,'XTick',1:6,'XTic
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Closed Loop system identification toolbox

 Hi all,

 
Is there any MATLAB Toolbox available for Closed Loop System Identification because MATLAB's current system identification toolkit is for identification of open loop systems?
 
I already knew one tool named CLOSID but it is not as flexible as MATLAB's sys ID toolkit and i am facing some problems in using it.
 
Is there anyway of using MATLAB's system identification toolkit for CLOSED LOOP SYSTEMS?

 NOTE:-


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A model in System Identification Toolbox represents the equation y = Gu + He where G is the transfer function between y and u and H is the transfer function between y and e. G is called the "measured" component and H the "noise component" of the overall transfer function between y and [u, e].
 
In state space case, the equation is:
 
x(k+1) = A x(k) + B u(k) + K e(k)
  y(k) = C x(k) + D u(k) + e(k)

H in this case is:

x(k+1) = A x(k) + K e(k)
  yh(k) = C x(k) + e(k)

When you estimate a state space model using PEM, you estimate the values of one or more of A, B, C, D and K. In order to have a nontrivial noise component in your model, make sure that K is not fixed at zero. You do this by using "DisturbanceModel"/'estimate' PV pair in the estimation command or by setting the DisturbanceModel property of an existing IDSS model to 'estimate':

model = pem(data, NX, 'dist', 'estimate')
model2 = idss(drss(NX)) % or some other template you created using IDSS command
model2.DisturbanceMOdel = 'estimate'
model2 = pem(data, model2, 'focus', 'prediction')
Now, the claim is that estimating K along with A, B, C, D helps identify the plant even though it was operating under closed loop. Why this is the case is best answered in the reference I mentioned before. Here is a simple description: if you are measuring signals right at the I/O ports of a plant, you are indeed measuring the effect of changes in input at the plant's output. However the input signal contains the portion of the past output (because of feedback). This by itself is not a problem. But you are not only feeding back the previous outputs but also any disturbances that might have affected those previous output values. This disturbance is thus correlated with the system inputs. Adding a sufficiently flexible "H" helps you separate out the effect of disturbances on the plant output.

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